Stock Prediction Sample

Introduction

This notebook is designed to be self-guided, broken out into a number of steps to help illustrate the process of developing a ML model.

!pip install prophet
import pyspark.sql.functions as F
from pyspark.sql.types import *

StatementMeta(, 969b99e8-3fbb-4fd8-83d4-d90942f1c689, 3, Finished, Available)

Collecting prophet
  Downloading prophet-1.1.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.4 MB)
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Collecting cmdstanpy>=1.0.4 (from prophet)
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  Downloading stanio-0.3.0-py3-none-any.whl (6.2 kB)
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Installing collected packages: stanio, cmdstanpy, prophet
Successfully installed cmdstanpy-1.2.0 prophet-1.1.5 stanio-0.3.0

# configuration for downloads and stock symbol to analyze

# url to source tar file
FULL_URL = "https://fabricrealtimelab.blob.core.windows.net/public/AbboCost_Stock_History/stockhistory-2023-2024.tgz?sp=r&st=2023-11-26T23:59:09Z&se=2027-11-27T07:59:09Z&spr=https&sv=2022-11-02&sr=b&sig=70w%2BT6ZVGpdTd6YJr%2FzPhKUFk9JYJ2ezu6%2BBBr9ahxc%3D"
# lakehouse location -- assumes default lakehouse
LAKEHOUSE_FOLDER = "/lakehouse/default"

# filename and data folders
TAR_FILE_NAME = "stockhistory-2023-2024.tgz"
DATA_FOLDER = "Files/stockhistory/raw"

TAR_FILE_PATH = f"/{LAKEHOUSE_FOLDER}/{DATA_FOLDER}/tar/"
CSV_FILE_PATH = f"/{LAKEHOUSE_FOLDER}/{DATA_FOLDER}/csv/"

# specify the stock symbol to analyze - WHO, WHAT, IDK, WHY, BCUZ, TMRW, TDY, IDGD
# recommended: WHO, BCUZ, IDGD

# STOCK_SYMBOL = "IDGD"
# STOCK_SYMBOL = "BCUZ"
STOCK_SYMBOL = "WHO"

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Step 1: Download the stock history source data files

Normally, this data would be ingested continuously into our lakehouse. In the interest of time, we've generated enough data to train from and do some experimentation. This was generated with the same engine (https://aka.ms/fabricrealtimelab); the stock generator is largely random and will vary by installation, but there are certain trends that should be present.

The cell will check for the data and only download/extract if the data does not exist.

import os

if not os.path.exists(LAKEHOUSE_FOLDER):
    # add a lakehouse if the notebook has no default lakehouse
    # a new notebook will not link to any lakehouse by default
    raise FileNotFoundError(
        "Lakehouse not found, please add a lakehouse for the notebook."
    )
else:
    # verify whether or not the required files are already in the lakehouse, and if not, download and unzip
    if not os.path.exists(f"{TAR_FILE_PATH}{TAR_FILE_NAME}"):
        os.makedirs(TAR_FILE_PATH, exist_ok=True)
        os.system(f"wget '{FULL_URL}' -O {TAR_FILE_PATH}{TAR_FILE_NAME}")

        #todo: better file checking
        os.makedirs(CSV_FILE_PATH, exist_ok=True)
        os.system(f"tar -zxvf {TAR_FILE_PATH}{TAR_FILE_NAME} -C {CSV_FILE_PATH}")

StatementMeta(, 969b99e8-3fbb-4fd8-83d4-d90942f1c689, 5, Finished, Available)

--2023-11-28 19:35:01--  https://fabricrealtimelab.blob.core.windows.net/public/AbboCost_Stock_History/stockhistory-2023-2024.tgz?sp=r&st=2023-11-26T23:59:09Z&se=2027-11-27T07:59:09Z&spr=https&sv=2022-11-02&sr=b&sig=70w%2BT6ZVGpdTd6YJr%2FzPhKUFk9JYJ2ezu6%2BBBr9ahxc%3D
Resolving fabricrealtimelab.blob.core.windows.net... 52.239.235.100
Connecting to fabricrealtimelab.blob.core.windows.net|52.239.235.100|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 49453874 (47M) [application/x-compressed]
Saving to: ‘//lakehouse/default/Files/stockhistory/raw/tar/stockhistory-2023-2024.tgz’

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  9500K .......... .......... .......... .......... .......... 19%  124M 4s
  9550K .......... .......... .......... .......... .......... 19%  117M 4s
  9600K .......... .......... .......... .......... .......... 19% 94.9M 4s
  9650K .......... .......... .......... .......... .......... 20%  123M 3s
  9700K .......... .......... .......... .......... .......... 20%  115M 3s
  9750K .......... .......... .......... .......... .......... 20% 1.73M 4s
  9800K .......... .......... .......... .......... .......... 20% 83.1M 4s
  9850K .......... .......... .......... .......... .......... 20% 99.3M 4s
  9900K .......... .......... .......... .......... .......... 20% 68.8M 3s
  9950K .......... .......... .......... .......... .......... 20%  103M 3s
 10000K .......... .......... .......... .......... .......... 20% 87.0M 3s
 10050K .......... .......... .......... .......... .......... 20%  117M 3s
 10100K .......... .......... .......... .......... .......... 21%  104M 3s
 10150K .......... .......... .......... .......... .......... 21%  122M 3s
 10200K .......... .......... .......... .......... .......... 21% 88.1M 3s
 10250K .......... .......... .......... .......... .......... 21%  109M 3s
 10300K .......... .......... .......... .......... .......... 21%  109M 3s
 10350K .......... .......... .......... .......... .......... 21%  101M 3s
 10400K .......... .......... .......... .......... .......... 21% 91.9M 3s
 10450K .......... .......... .......... .......... .......... 21%  109M 3s
 10500K .......... .......... .......... .......... .......... 21%  112M 3s
 10550K .......... .......... .......... .......... .......... 21%  106M 3s
 10600K .......... .......... .......... .......... .......... 22%  101M 3s
 10650K .......... .......... .......... .......... .......... 22%  105M 3s
 10700K .......... .......... .......... .......... .......... 22%  128M 3s
 10750K .......... .......... .......... .......... .......... 22%  125M 3s
 10800K .......... .......... .......... .......... .......... 22% 96.2M 3s
 10850K .......... .......... .......... .......... .......... 22%  118M 3s
 10900K .......... .......... .......... .......... .......... 22%  111M 3s
 10950K .......... .......... .......... .......... .......... 22%  141M 3s
 11000K .......... .......... .......... .......... .......... 22%  105M 3s
 11050K .......... .......... .......... .......... .......... 22%  127M 3s
 11100K .......... .......... .......... .......... .......... 23%  125M 3s
 11150K .......... .......... .......... .......... .......... 23%  118M 3s
 11200K .......... .......... .......... .......... .......... 23%  105M 3s
 11250K .......... .......... .......... .......... .......... 23%  124M 3s
 11300K .......... .......... .......... .......... .......... 23%  112M 3s
 11350K .......... .......... .......... .......... .......... 23%  135M 3s
 11400K .......... .......... .......... .......... .......... 23%  102M 3s
 11450K .......... .......... .......... .......... .......... 23% 1.84M 3s
 11500K .......... .......... .......... .......... .......... 23%  126M 3s
 11550K .......... .......... .......... .......... .......... 24%  135M 3s
 11600K .......... .......... .......... .......... .......... 24% 98.7M 3s
 11650K .......... .......... .......... .......... .......... 24%  145M 3s
 11700K .......... .......... .......... .......... .......... 24%  137M 3s
 11750K .......... .......... .......... .......... .......... 24%  134M 3s
 11800K .......... .......... .......... .......... .......... 24%  121M 3s
 11850K .......... .......... .......... .......... .......... 24%  126M 3s
 11900K .......... .......... .......... .......... .......... 24%  121M 3s
 11950K .......... .......... .......... .......... .......... 24%  132M 3s
 12000K .......... .......... .......... .......... .......... 24%  105M 3s
 12050K .......... .......... .......... .......... .......... 25%  128M 3s
 12100K .......... .......... .......... .......... .......... 25%  121M 3s
 12150K .......... .......... .......... .......... .......... 25%  121M 3s
 12200K .......... .......... .......... .......... .......... 25%  108M 3s
 12250K .......... .......... .......... .......... .......... 25% 1.03M 3s
 12300K .......... .......... .......... .......... .......... 25% 71.5M 3s
 12350K .......... .......... .......... .......... .......... 25% 91.8M 3s
 12400K .......... .......... .......... .......... .......... 25% 69.9M 3s
 12450K .......... .......... .......... .......... .......... 25% 71.3M 3s
 12500K .......... .......... .......... .......... .......... 25%  113M 3s
 12550K .......... .......... .......... .......... .......... 26%  121M 3s
 12600K .......... .......... .......... .......... .......... 26% 90.6M 3s
 12650K .......... .......... .......... .......... .......... 26%  112M 3s
 12700K .......... .......... .......... .......... .......... 26%  115M 3s
 12750K .......... .......... .......... .......... .......... 26%  106M 3s
 12800K .......... .......... .......... .......... .......... 26% 92.0M 3s
 12850K .......... .......... .......... .......... .......... 26%  124M 3s
 12900K .......... .......... .......... .......... .......... 26%  116M 3s
 12950K .......... .......... .......... .......... .......... 26%  106M 3s
 13000K .......... .......... .......... .......... .......... 27%  105M 3s
 13050K .......... .......... .......... .......... .......... 27%  108M 3s
 13100K .......... .......... .......... .......... .......... 27%  123M 3s
 13150K .......... .......... .......... .......... .......... 27%  121M 3s
 13200K .......... .......... .......... .......... .......... 27%  104M 3s
 13250K .......... .......... .......... .......... .......... 27%  121M 3s
 13300K .......... .......... .......... .......... .......... 27%  116M 3s
 13350K .......... .......... .......... .......... .......... 27%  123M 3s
 13400K .......... .......... .......... .......... .......... 27% 87.9M 3s
 13450K .......... .......... .......... .......... .......... 27%  102M 3s
 13500K .......... .......... .......... .......... .......... 28% 92.8M 3s
 13550K .......... .......... .......... .......... .......... 28%  123M 3s
 13600K .......... .......... .......... .......... .......... 28%  101M 3s
 13650K .......... .......... .......... .......... .......... 28%  111M 3s
 13700K .......... .......... .......... .......... .......... 28%  120M 3s
 13750K .......... .......... .......... .......... .......... 28%  103M 3s
 13800K .......... .......... .......... .......... .......... 28% 91.9M 3s
 13850K .......... .......... .......... .......... .......... 28%  102M 3s
 13900K .......... .......... .......... .......... .......... 28%  104M 3s
 13950K .......... .......... .......... .......... .......... 28%  114M 2s
 14000K .......... .......... .......... .......... .......... 29% 97.3M 2s
 14050K .......... .......... .......... .......... .......... 29%  118M 2s
 14100K .......... .......... .......... .......... .......... 29%  108M 2s
 14150K .......... .......... .......... .......... .......... 29%  115M 2s
 14200K .......... .......... .......... .......... .......... 29% 95.9M 2s
 14250K .......... .......... .......... .......... .......... 29% 2.08M 2s
 14300K .......... .......... .......... .......... .......... 29%  110M 2s
 14350K .......... .......... .......... .......... .......... 29% 92.2M 2s
 14400K .......... .......... .......... .......... .......... 29% 91.6M 2s
 14450K .......... .......... .......... .......... .......... 30% 79.6M 2s
 14500K .......... .......... .......... .......... .......... 30%  120M 2s
 14550K .......... .......... .......... .......... .......... 30%  121M 2s
 14600K .......... .......... .......... .......... .......... 30%  117M 2s
 14650K .......... .......... .......... .......... .......... 30%  135M 2s
 14700K .......... .......... .......... .......... .......... 30%  120M 2s
 14750K .......... .......... .......... .......... .......... 30%  119M 2s
 14800K .......... .......... .......... .......... .......... 30%  100M 2s
 14850K .......... .......... .......... .......... .......... 30%  126M 2s
 14900K .......... .......... .......... .......... .......... 30%  131M 2s
 14950K .......... .......... .......... .......... .......... 31%  126M 2s
 15000K .......... .......... .......... .......... .......... 31%  117M 2s
 15050K .......... .......... .......... .......... .......... 31%  135M 2s
 15100K .......... .......... .......... .......... .......... 31%  131M 2s
 15150K .......... .......... .......... .......... .......... 31%  135M 2s
 15200K .......... .......... .......... .......... .......... 31%  112M 2s
 15250K .......... .......... .......... .......... .......... 31%  129M 2s
 15300K .......... .......... .......... .......... .......... 31%  133M 2s
 15350K .......... .......... .......... .......... .......... 31%  127M 2s
 15400K .......... .......... .......... .......... .......... 31%  103M 2s
 15450K .......... .......... .......... .......... .......... 32%  120M 2s
 15500K .......... .......... .......... .......... .......... 32%  133M 2s
 15550K .......... .......... .......... .......... .......... 32%  119M 2s
 15600K .......... .......... .......... .......... .......... 32%  109M 2s
 15650K .......... .......... .......... .......... .......... 32%  132M 2s
 15700K .......... .......... .......... .......... .......... 32%  134M 2s
 15750K .......... .......... .......... .......... .......... 32%  132M 2s
 15800K .......... .......... .......... .......... .......... 32%  109M 2s
 15850K .......... .......... .......... .......... .......... 32%  128M 2s
 15900K .......... .......... .......... .......... .......... 33%  133M 2s
 15950K .......... .......... .......... .......... .......... 33%  135M 2s
 16000K .......... .......... .......... .......... .......... 33%  116M 2s
 16050K .......... .......... .......... .......... .......... 33% 1.78M 2s
 16100K .......... .......... .......... .......... .......... 33% 92.7M 2s
 16150K .......... .......... .......... .......... .......... 33%  126M 2s
 16200K .......... .......... .......... .......... .......... 33%  104M 2s
 16250K .......... .......... .......... .......... .......... 33%  128M 2s
 16300K .......... .......... .......... .......... .......... 33%  131M 2s
 16350K .......... .......... .......... .......... .......... 33%  989K 2s
 16400K .......... .......... .......... .......... .......... 34% 82.1M 2s
 16450K .......... .......... .......... .......... .......... 34%  106M 2s
 16500K .......... .......... .......... .......... .......... 34% 98.0M 2s
 16550K .......... .......... .......... .......... .......... 34%  116M 2s
 16600K .......... .......... .......... .......... .......... 34% 94.7M 2s
 16650K .......... .......... .......... .......... .......... 34%  115M 2s
 16700K .......... .......... .......... .......... .......... 34%  126M 2s
 16750K .......... .......... .......... .......... .......... 34%  123M 2s
 16800K .......... .......... .......... .......... .......... 34%  114M 2s
 16850K .......... .......... .......... .......... .......... 34%  118M 2s
 16900K .......... .......... .......... .......... .......... 35%  122M 2s
 16950K .......... .......... .......... .......... .......... 35%  128M 2s
 17000K .......... .......... .......... .......... .......... 35%  103M 2s
 17050K .......... .......... .......... .......... .......... 35%  125M 2s
 17100K .......... .......... .......... .......... .......... 35%  124M 2s
 17150K .......... .......... .......... .......... .......... 35%  111M 2s
 17200K .......... .......... .......... .......... .......... 35%  104M 2s
 17250K .......... .......... .......... .......... .......... 35%  111M 2s
 17300K .......... .......... .......... .......... .......... 35%  123M 2s
 17350K .......... .......... .......... .......... .......... 36%  117M 2s
 17400K .......... .......... .......... .......... .......... 36%  111M 2s
 17450K .......... .......... .......... .......... .......... 36%  129M 2s
 17500K .......... .......... .......... .......... .......... 36%  123M 2s
 17550K .......... .......... .......... .......... .......... 36%  132M 2s
 17600K .......... .......... .......... .......... .......... 36%  105M 2s
 17650K .......... .......... .......... .......... .......... 36%  107M 2s
 17700K .......... .......... .......... .......... .......... 36%  129M 2s
 17750K .......... .......... .......... .......... .......... 36%  115M 2s
 17800K .......... .......... .......... .......... .......... 36%  117M 2s
 17850K .......... .......... .......... .......... .......... 37%  134M 2s
 17900K .......... .......... .......... .......... .......... 37%  126M 2s
 17950K .......... .......... .......... .......... .......... 37%  128M 2s
 18000K .......... .......... .......... .......... .......... 37%  109M 2s
 18050K .......... .......... .......... .......... .......... 37%  123M 2s
 18100K .......... .......... .......... .......... .......... 37%  132M 2s
 18150K .......... .......... .......... .......... .......... 37%  110M 2s
 18200K .......... .......... .......... .......... .......... 37%  106M 2s
 18250K .......... .......... .......... .......... .......... 37%  124M 2s
 18300K .......... .......... .......... .......... .......... 37%  135M 2s
 18350K .......... .......... .......... .......... .......... 38%  132M 2s
 18400K .......... .......... .......... .......... .......... 38%  105M 2s
 18450K .......... .......... .......... .......... .......... 38%  120M 2s
 18500K .......... .......... .......... .......... .......... 38%  123M 2s
 18550K .......... .......... .......... .......... .......... 38%  120M 2s
 18600K .......... .......... .......... .......... .......... 38%  104M 2s
 18650K .......... .......... .......... .......... .......... 38%  108M 2s
 18700K .......... .......... .......... .......... .......... 38%  118M 2s
 18750K .......... .......... .......... .......... .......... 38% 2.20M 2s
 18800K .......... .......... .......... .......... .......... 39% 25.5M 2s
 18850K .......... .......... .......... .......... .......... 39% 60.8M 2s
 18900K .......... .......... .......... .......... .......... 39%  109M 2s
 18950K .......... .......... .......... .......... .......... 39%  102M 2s
 19000K .......... .......... .......... .......... .......... 39% 62.2M 2s
 19050K .......... .......... .......... .......... .......... 39% 93.9M 2s
 19100K .......... .......... .......... .......... .......... 39%  125M 2s
 19150K .......... .......... .......... .......... .......... 39%  120M 2s
 19200K .......... .......... .......... .......... .......... 39% 98.1M 2s
 19250K .......... .......... .......... .......... .......... 39%  127M 2s
 19300K .......... .......... .......... .......... .......... 40%  110M 2s
 19350K .......... .......... .......... .......... .......... 40%  135M 2s
 19400K .......... .......... .......... .......... .......... 40%  107M 2s
 19450K .......... .......... .......... .......... .......... 40%  112M 2s
 19500K .......... .......... .......... .......... .......... 40%  128M 2s
 19550K .......... .......... .......... .......... .......... 40%  116M 2s
 19600K .......... .......... .......... .......... .......... 40%  101M 2s
 19650K .......... .......... .......... .......... .......... 40%  127M 2s
 19700K .......... .......... .......... .......... .......... 40%  116M 2s
 19750K .......... .......... .......... .......... .......... 40%  148M 2s
 19800K .......... .......... .......... .......... .......... 41%  113M 2s
 19850K .......... .......... .......... .......... .......... 41%  117M 2s
 19900K .......... .......... .......... .......... .......... 41%  132M 2s
 19950K .......... .......... .......... .......... .......... 41%  121M 2s
 20000K .......... .......... .......... .......... .......... 41%  108M 2s
 20050K .......... .......... .......... .......... .......... 41%  126M 2s
 20100K .......... .......... .......... .......... .......... 41%  102M 2s
 20150K .......... .......... .......... .......... .......... 41%  128M 2s
 20200K .......... .......... .......... .......... .......... 41%  103M 2s
 20250K .......... .......... .......... .......... .......... 42%  127M 2s
 20300K .......... .......... .......... .......... .......... 42%  127M 2s
 20350K .......... .......... .......... .......... .......... 42% 1.84M 2s
 20400K .......... .......... .......... .......... .......... 42% 88.3M 2s
 20450K .......... .......... .......... .......... .......... 42%  945K 2s
 20500K .......... .......... .......... .......... .......... 42%  107M 2s
 20550K .......... .......... .......... .......... .......... 42% 98.0M 2s
 20600K .......... .......... .......... .......... .......... 42% 91.3M 2s
 20650K .......... .......... .......... .......... .......... 42% 92.3M 2s
 20700K .......... .......... .......... .......... .......... 42% 90.8M 2s
 20750K .......... .......... .......... .......... .......... 43% 92.0M 2s
 20800K .......... .......... .......... .......... .......... 43%  100M 2s
 20850K .......... .......... .......... .......... .......... 43%  123M 2s
 20900K .......... .......... .......... .......... .......... 43%  123M 2s
 20950K .......... .......... .......... .......... .......... 43%  130M 2s
 21000K .......... .......... .......... .......... .......... 43%  102M 2s
 21050K .......... .......... .......... .......... .......... 43%  127M 2s
 21100K .......... .......... .......... .......... .......... 43%  107M 2s
 21150K .......... .......... .......... .......... .......... 43%  124M 2s
 21200K .......... .......... .......... .......... .......... 44%  104M 2s
 21250K .......... .......... .......... .......... .......... 44%  115M 2s
 21300K .......... .......... .......... .......... .......... 44%  123M 2s
 21350K .......... .......... .......... .......... .......... 44%  120M 2s
 21400K .......... .......... .......... .......... .......... 44%  101M 2s
 21450K .......... .......... .......... .......... .......... 44%  121M 2s
 21500K .......... .......... .......... .......... .......... 44%  129M 2s
 21550K .......... .......... .......... .......... .......... 44%  121M 2s
 21600K .......... .......... .......... .......... .......... 44% 99.8M 2s
 21650K .......... .......... .......... .......... .......... 44%  121M 2s
 21700K .......... .......... .......... .......... .......... 45%  114M 2s
 21750K .......... .......... .......... .......... .......... 45%  116M 2s
 21800K .......... .......... .......... .......... .......... 45% 98.3M 2s
 21850K .......... .......... .......... .......... .......... 45%  117M 2s
 21900K .......... .......... .......... .......... .......... 45%  109M 2s
 21950K .......... .......... .......... .......... .......... 45%  110M 2s
 22000K .......... .......... .......... .......... .......... 45% 98.4M 2s
 22050K .......... .......... .......... .......... .......... 45%  130M 2s
 22100K .......... .......... .......... .......... .......... 45%  110M 2s
 22150K .......... .......... .......... .......... .......... 45%  122M 2s
 22200K .......... .......... .......... .......... .......... 46%  104M 2s
 22250K .......... .......... .......... .......... .......... 46%  113M 2s
 22300K .......... .......... .......... .......... .......... 46%  116M 1s
 22350K .......... .......... .......... .......... .......... 46%  110M 1s
 22400K .......... .......... .......... .......... .......... 46%  104M 1s
 22450K .......... .......... .......... .......... .......... 46%  103M 1s
 22500K .......... .......... .......... .......... .......... 46%  118M 1s
 22550K .......... .......... .......... .......... .......... 46%  117M 1s
 22600K .......... .......... .......... .......... .......... 46%  101M 1s
 22650K .......... .......... .......... .......... .......... 47%  113M 1s
 22700K .......... .......... .......... .......... .......... 47%  112M 1s
 22750K .......... .......... .......... .......... .......... 47%  125M 1s
 22800K .......... .......... .......... .......... .......... 47% 96.7M 1s
 22850K .......... .......... .......... .......... .......... 47%  113M 1s
 22900K .......... .......... .......... .......... .......... 47%  120M 1s
 22950K .......... .......... .......... .......... .......... 47%  114M 1s
 23000K .......... .......... .......... .......... .......... 47% 99.6M 1s
 23050K .......... .......... .......... .......... .......... 47% 93.1M 1s
 23100K .......... .......... .......... .......... .......... 47%  104M 1s
 23150K .......... .......... .......... .......... .......... 48%  119M 1s
 23200K .......... .......... .......... .......... .......... 48% 2.72M 1s
 23250K .......... .......... .......... .......... .......... 48%  111M 1s
 23300K .......... .......... .......... .......... .......... 48%  113M 1s
 23350K .......... .......... .......... .......... .......... 48% 91.3M 1s
 23400K .......... .......... .......... .......... .......... 48% 92.5M 1s
 23450K .......... .......... .......... .......... .......... 48% 89.6M 1s
 23500K .......... .......... .......... .......... .......... 48%  106M 1s
 23550K .......... .......... .......... .......... .......... 48% 99.2M 1s
 23600K .......... .......... .......... .......... .......... 48% 93.4M 1s
 23650K .......... .......... .......... .......... .......... 49%  110M 1s
 23700K .......... .......... .......... .......... .......... 49%  101M 1s
 23750K .......... .......... .......... .......... .......... 49% 97.4M 1s
 23800K .......... .......... .......... .......... .......... 49% 99.9M 1s
 23850K .......... .......... .......... .......... .......... 49%  124M 1s
 23900K .......... .......... .......... .......... .......... 49%  111M 1s
 23950K .......... .......... .......... .......... .......... 49%  122M 1s
 24000K .......... .......... .......... .......... .......... 49% 86.3M 1s
 24050K .......... .......... .......... .......... .......... 49%  118M 1s
 24100K .......... .......... .......... .......... .......... 50%  109M 1s
 24150K .......... .......... .......... .......... .......... 50%  107M 1s
 24200K .......... .......... .......... .......... .......... 50%  101M 1s
 24250K .......... .......... .......... .......... .......... 50%  107M 1s
 24300K .......... .......... .......... .......... .......... 50%  128M 1s
 24350K .......... .......... .......... .......... .......... 50%  114M 1s
 24400K .......... .......... .......... .......... .......... 50% 93.4M 1s
 24450K .......... .......... .......... .......... .......... 50%  110M 1s
 24500K .......... .......... .......... .......... .......... 50%  121M 1s
 24550K .......... .......... .......... .......... .......... 50% 1.09M 1s
 24600K .......... .......... .......... .......... .......... 51% 93.3M 1s
 24650K .......... .......... .......... .......... .......... 51%  112M 1s
 24700K .......... .......... .......... .......... .......... 51%  121M 1s
 24750K .......... .......... .......... .......... .......... 51%  117M 1s
 24800K .......... .......... .......... .......... .......... 51% 94.4M 1s
 24850K .......... .......... .......... .......... .......... 51%  104M 1s
 24900K .......... .......... .......... .......... .......... 51%  121M 1s
 24950K .......... .......... .......... .......... .......... 51%  115M 1s
 25000K .......... .......... .......... .......... .......... 51%  108M 1s
 25050K .......... .......... .......... .......... .......... 51%  135M 1s
 25100K .......... .......... .......... .......... .......... 52%  132M 1s
 25150K .......... .......... .......... .......... .......... 52%  116M 1s
 25200K .......... .......... .......... .......... .......... 52% 99.9M 1s
 25250K .......... .......... .......... .......... .......... 52%  120M 1s
 25300K .......... .......... .......... .......... .......... 52%  131M 1s
 25350K .......... .......... .......... .......... .......... 52%  119M 1s
 25400K .......... .......... .......... .......... .......... 52% 96.3M 1s
 25450K .......... .......... .......... .......... .......... 52%  119M 1s
 25500K .......... .......... .......... .......... .......... 52%  114M 1s
 25550K .......... .......... .......... .......... .......... 53%  118M 1s
 25600K .......... .......... .......... .......... .......... 53%  100M 1s
 25650K .......... .......... .......... .......... .......... 53%  132M 1s
 25700K .......... .......... .......... .......... .......... 53%  112M 1s
 25750K .......... .......... .......... .......... .......... 53%  118M 1s
 25800K .......... .......... .......... .......... .......... 53%  106M 1s
 25850K .......... .......... .......... .......... .......... 53%  102M 1s
 25900K .......... .......... .......... .......... .......... 53%  122M 1s
 25950K .......... .......... .......... .......... .......... 53%  115M 1s
 26000K .......... .......... .......... .......... .......... 53%  103M 1s
 26050K .......... .......... .......... .......... .......... 54%  125M 1s
 26100K .......... .......... .......... .......... .......... 54%  126M 1s
 26150K .......... .......... .......... .......... .......... 54%  127M 1s
 26200K .......... .......... .......... .......... .......... 54%  100M 1s
 26250K .......... .......... .......... .......... .......... 54%  112M 1s
 26300K .......... .......... .......... .......... .......... 54%  109M 1s
 26350K .......... .......... .......... .......... .......... 54%  111M 1s
 26400K .......... .......... .......... .......... .......... 54%  102M 1s
 26450K .......... .......... .......... .......... .......... 54%  112M 1s
 26500K .......... .......... .......... .......... .......... 54%  112M 1s
 26550K .......... .......... .......... .......... .......... 55%  109M 1s
 26600K .......... .......... .......... .......... .......... 55% 94.7M 1s
 26650K .......... .......... .......... .......... .......... 55%  119M 1s
 26700K .......... .......... .......... .......... .......... 55%  116M 1s
 26750K .......... .......... .......... .......... .......... 55%  119M 1s
 26800K .......... .......... .......... .......... .......... 55% 99.5M 1s
 26850K .......... .......... .......... .......... .......... 55%  112M 1s
 26900K .......... .......... .......... .......... .......... 55% 2.22M 1s
 26950K .......... .......... .......... .......... .......... 55% 72.6M 1s
 27000K .......... .......... .......... .......... .......... 56% 74.7M 1s
 27050K .......... .......... .......... .......... .......... 56%  106M 1s
 27100K .......... .......... .......... .......... .......... 56% 82.2M 1s
 27150K .......... .......... .......... .......... .......... 56%  114M 1s
 27200K .......... .......... .......... .......... .......... 56% 95.9M 1s
 27250K .......... .......... .......... .......... .......... 56%  105M 1s
 27300K .......... .......... .......... .......... .......... 56%  107M 1s
 27350K .......... .......... .......... .......... .......... 56%  116M 1s
 27400K .......... .......... .......... .......... .......... 56% 94.4M 1s
 27450K .......... .......... .......... .......... .......... 56%  124M 1s
 27500K .......... .......... .......... .......... .......... 57%  125M 1s
 27550K .......... .......... .......... .......... .......... 57%  100M 1s
 27600K .......... .......... .......... .......... .......... 57% 99.2M 1s
 27650K .......... .......... .......... .......... .......... 57%  115M 1s
 27700K .......... .......... .......... .......... .......... 57%  111M 1s
 27750K .......... .......... .......... .......... .......... 57%  112M 1s
 27800K .......... .......... .......... .......... .......... 57% 96.2M 1s
 27850K .......... .......... .......... .......... .......... 57%  107M 1s
 27900K .......... .......... .......... .......... .......... 57%  107M 1s
 27950K .......... .......... .......... .......... .......... 57%  118M 1s
 28000K .......... .......... .......... .......... .......... 58% 89.6M 1s
 28050K .......... .......... .......... .......... .......... 58%  108M 1s
 28100K .......... .......... .......... .......... .......... 58%  128M 1s
 28150K .......... .......... .......... .......... .......... 58% 98.5M 1s
 28200K .......... .......... .......... .......... .......... 58% 94.9M 1s
 28250K .......... .......... .......... .......... .......... 58%  120M 1s
 28300K .......... .......... .......... .......... .......... 58%  105M 1s
 28350K .......... .......... .......... .......... .......... 58%  114M 1s
 28400K .......... .......... .......... .......... .......... 58% 97.8M 1s
 28450K .......... .......... .......... .......... .......... 59%  125M 1s
 28500K .......... .......... .......... .......... .......... 59%  117M 1s
 28550K .......... .......... .......... .......... .......... 59% 1.83M 1s
 28600K .......... .......... .......... .......... .......... 59% 94.2M 1s
 28650K .......... .......... .......... .......... .......... 59%  973K 1s
 28700K .......... .......... .......... .......... .......... 59% 81.8M 1s
 28750K .......... .......... .......... .......... .......... 59%  120M 1s
 28800K .......... .......... .......... .......... .......... 59% 90.3M 1s
 28850K .......... .......... .......... .......... .......... 59%  112M 1s
 28900K .......... .......... .......... .......... .......... 59%  102M 1s
 28950K .......... .......... .......... .......... .......... 60%  102M 1s
 29000K .......... .......... .......... .......... .......... 60% 90.6M 1s
 29050K .......... .......... .......... .......... .......... 60%  103M 1s
 29100K .......... .......... .......... .......... .......... 60%  116M 1s
 29150K .......... .......... .......... .......... .......... 60%  110M 1s
 29200K .......... .......... .......... .......... .......... 60% 96.9M 1s
 29250K .......... .......... .......... .......... .......... 60%  109M 1s
 29300K .......... .......... .......... .......... .......... 60%  126M 1s
 29350K .......... .......... .......... .......... .......... 60%  141M 1s
 29400K .......... .......... .......... .......... .......... 60%  114M 1s
 29450K .......... .......... .......... .......... .......... 61%  141M 1s
 29500K .......... .......... .......... .......... .......... 61%  133M 1s
 29550K .......... .......... .......... .......... .......... 61%  114M 1s
 29600K .......... .......... .......... .......... .......... 61%  115M 1s
 29650K .......... .......... .......... .......... .......... 61%  123M 1s
 29700K .......... .......... .......... .......... .......... 61%  121M 1s
 29750K .......... .......... .......... .......... .......... 61%  123M 1s
 29800K .......... .......... .......... .......... .......... 61%  113M 1s
 29850K .......... .......... .......... .......... .......... 61%  120M 1s
 29900K .......... .......... .......... .......... .......... 62%  128M 1s
 29950K .......... .......... .......... .......... .......... 62%  111M 1s
 30000K .......... .......... .......... .......... .......... 62%  112M 1s
 30050K .......... .......... .......... .......... .......... 62%  124M 1s
 30100K .......... .......... .......... .......... .......... 62%  129M 1s
 30150K .......... .......... .......... .......... .......... 62%  133M 1s
 30200K .......... .......... .......... .......... .......... 62%  113M 1s
 30250K .......... .......... .......... .......... .......... 62%  135M 1s
 30300K .......... .......... .......... .......... .......... 62%  131M 1s
 30350K .......... .......... .......... .......... .......... 62%  137M 1s
 30400K .......... .......... .......... .......... .......... 63%  115M 1s
 30450K .......... .......... .......... .......... .......... 63%  126M 1s
 30500K .......... .......... .......... .......... .......... 63%  121M 1s
 30550K .......... .......... .......... .......... .......... 63%  127M 1s
 30600K .......... .......... .......... .......... .......... 63%  112M 1s
 30650K .......... .......... .......... .......... .......... 63%  108M 1s
 30700K .......... .......... .......... .......... .......... 63%  112M 1s
 30750K .......... .......... .......... .......... .......... 63%  112M 1s
 30800K .......... .......... .......... .......... .......... 63% 89.6M 1s
 30850K .......... .......... .......... .......... .......... 63%  119M 1s
 30900K .......... .......... .......... .......... .......... 64%  129M 1s
 30950K .......... .......... .......... .......... .......... 64%  119M 1s
 31000K .......... .......... .......... .......... .......... 64% 99.0M 1s
 31050K .......... .......... .......... .......... .......... 64%  124M 1s
 31100K .......... .......... .......... .......... .......... 64%  124M 1s
 31150K .......... .......... .......... .......... .......... 64%  122M 1s
 31200K .......... .......... .......... .......... .......... 64%  102M 1s
 31250K .......... .......... .......... .......... .......... 64%  121M 1s
 31300K .......... .......... .......... .......... .......... 64%  118M 1s
 31350K .......... .......... .......... .......... .......... 65%  119M 1s
 31400K .......... .......... .......... .......... .......... 65% 99.3M 1s
 31450K .......... .......... .......... .......... .......... 65%  124M 1s
 31500K .......... .......... .......... .......... .......... 65%  109M 1s
 31550K .......... .......... .......... .......... .......... 65%  115M 1s
 31600K .......... .......... .......... .......... .......... 65% 87.6M 1s
 31650K .......... .......... .......... .......... .......... 65% 2.94M 1s
 31700K .......... .......... .......... .......... .......... 65% 98.1M 1s
 31750K .......... .......... .......... .......... .......... 65% 97.8M 1s
 31800K .......... .......... .......... .......... .......... 65% 78.4M 1s
 31850K .......... .......... .......... .......... .......... 66% 86.1M 1s
 31900K .......... .......... .......... .......... .......... 66% 95.0M 1s
 31950K .......... .......... .......... .......... .......... 66%  102M 1s
 32000K .......... .......... .......... .......... .......... 66%  104M 1s
 32050K .......... .......... .......... .......... .......... 66%  122M 1s
 32100K .......... .......... .......... .......... .......... 66%  106M 1s
 32150K .......... .......... .......... .......... .......... 66% 92.7M 1s
 32200K .......... .......... .......... .......... .......... 66% 95.8M 1s
 32250K .......... .......... .......... .......... .......... 66%  109M 1s
 32300K .......... .......... .......... .......... .......... 66%  109M 1s
 32350K .......... .......... .......... .......... .......... 67%  112M 1s
 32400K .......... .......... .......... .......... .......... 67% 99.9M 1s
 32450K .......... .......... .......... .......... .......... 67%  124M 1s
 32500K .......... .......... .......... .......... .......... 67%  121M 1s
 32550K .......... .......... .......... .......... .......... 67%  118M 1s
 32600K .......... .......... .......... .......... .......... 67%  112M 1s
 32650K .......... .......... .......... .......... .......... 67%  109M 1s
 32700K .......... .......... .......... .......... .......... 67%  108M 1s
 32750K .......... .......... .......... .......... .......... 67% 1.09M 1s
 32800K .......... .......... .......... .......... .......... 68% 89.9M 1s
 32850K .......... .......... .......... .......... .......... 68%  105M 1s
 32900K .......... .......... .......... .......... .......... 68% 92.8M 1s
 32950K .......... .......... .......... .......... .......... 68% 83.3M 1s
 33000K .......... .......... .......... .......... .......... 68% 80.5M 1s
 33050K .......... .......... .......... .......... .......... 68%  112M 1s
 33100K .......... .......... .......... .......... .......... 68%  116M 1s
 33150K .......... .......... .......... .......... .......... 68%  108M 1s
 33200K .......... .......... .......... .......... .......... 68% 88.5M 1s
 33250K .......... .......... .......... .......... .......... 68%  108M 1s
 33300K .......... .......... .......... .......... .......... 69%  112M 1s
 33350K .......... .......... .......... .......... .......... 69%  109M 1s
 33400K .......... .......... .......... .......... .......... 69% 95.1M 1s
 33450K .......... .......... .......... .......... .......... 69%  107M 1s
 33500K .......... .......... .......... .......... .......... 69%  105M 1s
 33550K .......... .......... .......... .......... .......... 69%  104M 1s
 33600K .......... .......... .......... .......... .......... 69% 82.1M 1s
 33650K .......... .......... .......... .......... .......... 69%  101M 1s
 33700K .......... .......... .......... .......... .......... 69%  105M 1s
 33750K .......... .......... .......... .......... .......... 69% 77.6M 1s
 33800K .......... .......... .......... .......... .......... 70% 77.0M 1s
 33850K .......... .......... .......... .......... .......... 70%  102M 1s
 33900K .......... .......... .......... .......... .......... 70%  102M 1s
 33950K .......... .......... .......... .......... .......... 70%  100M 1s
 34000K .......... .......... .......... .......... .......... 70% 86.5M 1s
 34050K .......... .......... .......... .......... .......... 70%  104M 1s
 34100K .......... .......... .......... .......... .......... 70% 95.3M 1s
 34150K .......... .......... .......... .......... .......... 70%  105M 1s
 34200K .......... .......... .......... .......... .......... 70% 78.0M 1s
 34250K .......... .......... .......... .......... .......... 71%  111M 1s
 34300K .......... .......... .......... .......... .......... 71%  103M 1s
 34350K .......... .......... .......... .......... .......... 71% 90.3M 1s
 34400K .......... .......... .......... .......... .......... 71% 85.4M 1s
 34450K .......... .......... .......... .......... .......... 71% 97.6M 1s
 34500K .......... .......... .......... .......... .......... 71%  101M 1s
 34550K .......... .......... .......... .......... .......... 71% 96.7M 1s
 34600K .......... .......... .......... .......... .......... 71% 79.5M 1s
 34650K .......... .......... .......... .......... .......... 71%  103M 1s
 34700K .......... .......... .......... .......... .......... 71%  102M 1s
 34750K .......... .......... .......... .......... .......... 72%  106M 1s
 34800K .......... .......... .......... .......... .......... 72% 84.2M 1s
 34850K .......... .......... .......... .......... .......... 72% 99.5M 1s
 34900K .......... .......... .......... .......... .......... 72%  111M 1s
 34950K .......... .......... .......... .......... .......... 72%  108M 1s
 35000K .......... .......... .......... .......... .......... 72%  108M 1s
 35050K .......... .......... .......... .......... .......... 72% 97.4M 1s
 35100K .......... .......... .......... .......... .......... 72%  108M 1s
 35150K .......... .......... .......... .......... .......... 72%  109M 1s
 35200K .......... .......... .......... .......... .......... 72% 92.4M 1s
 35250K .......... .......... .......... .......... .......... 73% 2.78M 1s
 35300K .......... .......... .......... .......... .......... 73% 90.6M 1s
 35350K .......... .......... .......... .......... .......... 73% 94.5M 1s
 35400K .......... .......... .......... .......... .......... 73% 82.0M 1s
 35450K .......... .......... .......... .......... .......... 73%  102M 1s
 35500K .......... .......... .......... .......... .......... 73%  116M 1s
 35550K .......... .......... .......... .......... .......... 73%  109M 1s
 35600K .......... .......... .......... .......... .......... 73% 87.1M 1s
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 35850K .......... .......... .......... .......... .......... 74%  116M 1s
 35900K .......... .......... .......... .......... .......... 74% 98.1M 1s
 35950K .......... .......... .......... .......... .......... 74% 93.0M 1s
 36000K .......... .......... .......... .......... .......... 74% 82.1M 1s
 36050K .......... .......... .......... .......... .......... 74% 96.8M 1s
 36100K .......... .......... .......... .......... .......... 74%  103M 1s
 36150K .......... .......... .......... .......... .......... 74%  103M 1s
 36200K .......... .......... .......... .......... .......... 75% 89.4M 1s
 36250K .......... .......... .......... .......... .......... 75% 97.1M 1s
 36300K .......... .......... .......... .......... .......... 75%  101M 1s
 36350K .......... .......... .......... .......... .......... 75%  108M 1s
 36400K .......... .......... .......... .......... .......... 75% 81.2M 1s
 36450K .......... .......... .......... .......... .......... 75%  101M 1s
 36500K .......... .......... .......... .......... .......... 75% 97.1M 1s
 36550K .......... .......... .......... .......... .......... 75%  102M 1s
 36600K .......... .......... .......... .......... .......... 75% 90.0M 1s
 36650K .......... .......... .......... .......... .......... 75%  103M 1s
 36700K .......... .......... .......... .......... .......... 76%  103M 1s
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 36800K .......... .......... .......... .......... .......... 76% 1.79M 1s
 36850K .......... .......... .......... .......... .......... 76%  880K 1s
 36900K .......... .......... .......... .......... .......... 76% 90.4M 1s
 36950K .......... .......... .......... .......... .......... 76% 98.5M 1s
 37000K .......... .......... .......... .......... .......... 76% 72.6M 1s
 37050K .......... .......... .......... .......... .......... 76% 89.4M 1s
 37100K .......... .......... .......... .......... .......... 76% 92.9M 1s
 37150K .......... .......... .......... .......... .......... 77% 84.1M 1s
 37200K .......... .......... .......... .......... .......... 77% 81.3M 1s
 37250K .......... .......... .......... .......... .......... 77% 99.9M 1s
 37300K .......... .......... .......... .......... .......... 77% 95.1M 1s
 37350K .......... .......... .......... .......... .......... 77% 94.4M 1s
 37400K .......... .......... .......... .......... .......... 77% 88.0M 1s
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 37500K .......... .......... .......... .......... .......... 77%  111M 1s
 37550K .......... .......... .......... .......... .......... 77%  101M 0s
 37600K .......... .......... .......... .......... .......... 77% 81.5M 0s
 37650K .......... .......... .......... .......... .......... 78% 96.4M 0s
 37700K .......... .......... .......... .......... .......... 78%  100M 0s
 37750K .......... .......... .......... .......... .......... 78%  119M 0s
 37800K .......... .......... .......... .......... .......... 78% 89.8M 0s
 37850K .......... .......... .......... .......... .......... 78% 92.6M 0s
 37900K .......... .......... .......... .......... .......... 78%  102M 0s
 37950K .......... .......... .......... .......... .......... 78% 97.6M 0s
 38000K .......... .......... .......... .......... .......... 78% 84.6M 0s
 38050K .......... .......... .......... .......... .......... 78%  114M 0s
 38100K .......... .......... .......... .......... .......... 78%  116M 0s
 38150K .......... .......... .......... .......... .......... 79%  108M 0s
 38200K .......... .......... .......... .......... .......... 79% 79.6M 0s
 38250K .......... .......... .......... .......... .......... 79% 97.9M 0s
 38300K .......... .......... .......... .......... .......... 79% 93.6M 0s
 38350K .......... .......... .......... .......... .......... 79% 99.7M 0s
 38400K .......... .......... .......... .......... .......... 79% 92.0M 0s
 38450K .......... .......... .......... .......... .......... 79% 91.5M 0s
 38500K .......... .......... .......... .......... .......... 79%  115M 0s
 38550K .......... .......... .......... .......... .......... 79%  103M 0s
 38600K .......... .......... .......... .......... .......... 80% 97.2M 0s
 38650K .......... .......... .......... .......... .......... 80%  101M 0s
 38700K .......... .......... .......... .......... .......... 80%  122M 0s
 38750K .......... .......... .......... .......... .......... 80%  114M 0s
 38800K .......... .......... .......... .......... .......... 80% 92.4M 0s
 38850K .......... .......... .......... .......... .......... 80%  111M 0s
 38900K .......... .......... .......... .......... .......... 80%  118M 0s
 38950K .......... .......... .......... .......... .......... 80%  122M 0s
 39000K .......... .......... .......... .......... .......... 80% 93.8M 0s
 39050K .......... .......... .......... .......... .......... 80%  108M 0s
 39100K .......... .......... .......... .......... .......... 81%  111M 0s
 39150K .......... .......... .......... .......... .......... 81%  101M 0s
 39200K .......... .......... .......... .......... .......... 81% 85.1M 0s
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 40300K .......... .......... .......... .......... .......... 83%  117M 0s
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 40400K .......... .......... .......... .......... .......... 83% 84.2M 0s
 40450K .......... .......... .......... .......... .......... 83% 94.8M 0s
 40500K .......... .......... .......... .......... .......... 83% 99.8M 0s
 40550K .......... .......... .......... .......... .......... 84%  112M 0s
 40600K .......... .......... .......... .......... .......... 84% 79.4M 0s
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 40800K .......... .......... .......... .......... .......... 84% 76.2M 0s
 40850K .......... .......... .......... .......... .......... 84% 98.1M 0s
 40900K .......... .......... .......... .......... .......... 84%  101M 0s
 40950K .......... .......... .......... .......... .......... 84% 1.13M 0s
 41000K .......... .......... .......... .......... .......... 84% 95.3M 0s
 41050K .......... .......... .......... .......... .......... 85% 78.4M 0s
 41100K .......... .......... .......... .......... .......... 85% 86.7M 0s
 41150K .......... .......... .......... .......... .......... 85% 99.3M 0s
 41200K .......... .......... .......... .......... .......... 85% 82.9M 0s
 41250K .......... .......... .......... .......... .......... 85%  117M 0s
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 41400K .......... .......... .......... .......... .......... 85% 99.2M 0s
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 41500K .......... .......... .......... .......... .......... 86%  114M 0s
 41550K .......... .......... .......... .......... .......... 86%  122M 0s
 41600K .......... .......... .......... .......... .......... 86% 95.1M 0s
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 41900K .......... .......... .......... .......... .......... 86%  125M 0s
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 46650K .......... .......... .......... .......... .......... 96%  131M 0s
 46700K .......... .......... .......... .......... .......... 96%  113M 0s
 46750K .......... .......... .......... .......... .......... 96%  118M 0s
 46800K .......... .......... .......... .......... .......... 97% 97.9M 0s
 46850K .......... .......... .......... .......... .......... 97%  119M 0s
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 46950K .......... .......... .......... .......... .......... 97%  105M 0s
 47000K .......... .......... .......... .......... .......... 97%  103M 0s
 47050K .......... .......... .......... .......... .......... 97%  110M 0s
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 47150K .......... .......... .......... .......... .......... 97%  111M 0s
 47200K .......... .......... .......... .......... .......... 97% 97.6M 0s
 47250K .......... .......... .......... .......... .......... 97%  113M 0s
 47300K .......... .......... .......... .......... .......... 98%  125M 0s
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 47400K .......... .......... .......... .......... .......... 98%  100M 0s
 47450K .......... .......... .......... .......... .......... 98%  125M 0s
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 47600K .......... .......... .......... .......... .......... 98%  110M 0s
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 47750K .......... .......... .......... .......... .......... 98%  111M 0s
 47800K .......... .......... .......... .......... .......... 99%  110M 0s
 47850K .......... .......... .......... .......... .......... 99%  138M 0s
 47900K .......... .......... .......... .......... .......... 99%  114M 0s
 47950K .......... .......... .......... .......... .......... 99%  123M 0s
 48000K .......... .......... .......... .......... .......... 99% 91.9M 0s
 48050K .......... .......... .......... .......... .......... 99% 2.16M 0s
 48100K .......... .......... .......... .......... .......... 99% 91.9M 0s
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 48200K .......... .......... .......... .......... .......... 99%  103M 0s
 48250K .......... .......... .......... .......... ....      100%  102M=2.0s

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./2023/05/Stocks-2023-05-04.csv
./2023/05/Stocks-2023-05-05.csv
./2023/05/Stocks-2023-05-06.csv
./2023/05/Stocks-2023-05-07.csv
./2023/05/Stocks-2023-05-08.csv
./2023/05/Stocks-2023-05-09.csv
./2023/05/Stocks-2023-05-10.csv
./2023/05/Stocks-2023-05-11.csv
./2023/05/Stocks-2023-05-12.csv
./2023/05/Stocks-2023-05-13.csv
./2023/05/Stocks-2023-05-14.csv
./2023/05/Stocks-2023-05-15.csv
./2023/05/Stocks-2023-05-16.csv
./2023/05/Stocks-2023-05-17.csv
./2023/05/Stocks-2023-05-18.csv
./2023/05/Stocks-2023-05-19.csv
./2023/05/Stocks-2023-05-20.csv
./2023/05/Stocks-2023-05-21.csv
./2023/05/Stocks-2023-05-22.csv
./2023/05/Stocks-2023-05-23.csv
./2023/05/Stocks-2023-05-24.csv
./2023/05/Stocks-2023-05-25.csv
./2023/05/Stocks-2023-05-26.csv
./2023/05/Stocks-2023-05-27.csv
./2023/05/Stocks-2023-05-28.csv
./2023/05/Stocks-2023-05-29.csv
./2023/05/Stocks-2023-05-30.csv
./2023/05/Stocks-2023-05-31.csv
./2023/04/Stocks-2023-04-01.csv
./2023/04/Stocks-2023-04-02.csv
./2023/04/Stocks-2023-04-03.csv
./2023/04/Stocks-2023-04-04.csv
./2023/04/Stocks-2023-04-05.csv
./2023/04/Stocks-2023-04-06.csv
./2023/04/Stocks-2023-04-07.csv
./2023/04/Stocks-2023-04-08.csv
./2023/04/Stocks-2023-04-09.csv
./2023/04/Stocks-2023-04-10.csv
./2023/04/Stocks-2023-04-11.csv
./2023/04/Stocks-2023-04-12.csv
./2023/04/Stocks-2023-04-13.csv
./2023/04/Stocks-2023-04-14.csv
./2023/04/Stocks-2023-04-15.csv
./2023/04/Stocks-2023-04-16.csv
./2023/04/Stocks-2023-04-17.csv
./2023/04/Stocks-2023-04-18.csv
./2023/04/Stocks-2023-04-19.csv
./2023/04/Stocks-2023-04-20.csv
./2023/04/Stocks-2023-04-21.csv
./2023/04/Stocks-2023-04-22.csv
./2023/04/Stocks-2023-04-23.csv
./2023/04/Stocks-2023-04-24.csv
./2023/04/Stocks-2023-04-25.csv
./2023/04/Stocks-2023-04-26.csv
./2023/04/Stocks-2023-04-27.csv
./2023/04/Stocks-2023-04-28.csv
./2023/04/Stocks-2023-04-29.csv
./2023/04/Stocks-2023-04-30.csv
./2023/03/Stocks-2023-03-01.csv
./2023/03/Stocks-2023-03-02.csv
./2023/03/Stocks-2023-03-03.csv
./2023/03/Stocks-2023-03-04.csv
./2023/03/Stocks-2023-03-05.csv
./2023/03/Stocks-2023-03-06.csv
./2023/03/Stocks-2023-03-07.csv
./2023/03/Stocks-2023-03-08.csv
./2023/03/Stocks-2023-03-09.csv
./2023/03/Stocks-2023-03-10.csv
./2023/03/Stocks-2023-03-11.csv
./2023/03/Stocks-2023-03-12.csv
./2023/03/Stocks-2023-03-13.csv
./2023/03/Stocks-2023-03-14.csv
./2023/03/Stocks-2023-03-15.csv
./2023/03/Stocks-2023-03-16.csv
./2023/03/Stocks-2023-03-17.csv
./2023/03/Stocks-2023-03-18.csv
./2023/03/Stocks-2023-03-19.csv
./2023/03/Stocks-2023-03-20.csv
./2023/03/Stocks-2023-03-21.csv
./2023/03/Stocks-2023-03-22.csv
./2023/03/Stocks-2023-03-23.csv
./2023/03/Stocks-2023-03-24.csv
./2023/03/Stocks-2023-03-25.csv
./2023/03/Stocks-2023-03-26.csv
./2023/03/Stocks-2023-03-27.csv
./2023/03/Stocks-2023-03-28.csv
./2023/03/Stocks-2023-03-29.csv
./2023/03/Stocks-2023-03-30.csv
./2023/03/Stocks-2023-03-31.csv
./2023/02/Stocks-2023-02-01.csv
./2023/02/Stocks-2023-02-02.csv
./2023/02/Stocks-2023-02-03.csv
./2023/02/Stocks-2023-02-04.csv
./2023/02/Stocks-2023-02-05.csv
./2023/02/Stocks-2023-02-06.csv
./2023/02/Stocks-2023-02-07.csv
./2023/02/Stocks-2023-02-08.csv
./2023/02/Stocks-2023-02-09.csv
./2023/02/Stocks-2023-02-10.csv
./2023/02/Stocks-2023-02-11.csv
./2023/02/Stocks-2023-02-12.csv
./2023/02/Stocks-2023-02-13.csv
./2023/02/Stocks-2023-02-14.csv
./2023/02/Stocks-2023-02-15.csv
./2023/02/Stocks-2023-02-16.csv
./2023/02/Stocks-2023-02-17.csv
./2023/02/Stocks-2023-02-18.csv
./2023/02/Stocks-2023-02-19.csv
./2023/02/Stocks-2023-02-20.csv
./2023/02/Stocks-2023-02-21.csv
./2023/02/Stocks-2023-02-22.csv
./2023/02/Stocks-2023-02-23.csv
./2023/02/Stocks-2023-02-24.csv
./2023/02/Stocks-2023-02-25.csv
./2023/02/Stocks-2023-02-26.csv
./2023/02/Stocks-2023-02-27.csv
./2023/02/Stocks-2023-02-28.csv
./2023/01/Stocks-2023-01-01.csv
./2023/01/Stocks-2023-01-02.csv
./2023/01/Stocks-2023-01-03.csv
./2023/01/Stocks-2023-01-04.csv
./2023/01/Stocks-2023-01-05.csv
./2023/01/Stocks-2023-01-06.csv
./2023/01/Stocks-2023-01-07.csv
./2023/01/Stocks-2023-01-08.csv
./2023/01/Stocks-2023-01-09.csv
./2023/01/Stocks-2023-01-10.csv
./2023/01/Stocks-2023-01-11.csv
./2023/01/Stocks-2023-01-12.csv
./2023/01/Stocks-2023-01-13.csv
./2023/01/Stocks-2023-01-14.csv
./2023/01/Stocks-2023-01-15.csv
./2023/01/Stocks-2023-01-16.csv
./2023/01/Stocks-2023-01-17.csv
./2023/01/Stocks-2023-01-18.csv
./2023/01/Stocks-2023-01-19.csv
./2023/01/Stocks-2023-01-20.csv
./2023/01/Stocks-2023-01-21.csv
./2023/01/Stocks-2023-01-22.csv
./2023/01/Stocks-2023-01-23.csv
./2023/01/Stocks-2023-01-24.csv
./2023/01/Stocks-2023-01-25.csv
./2023/01/Stocks-2023-01-26.csv
./2023/01/Stocks-2023-01-27.csv
./2023/01/Stocks-2023-01-28.csv
./2023/01/Stocks-2023-01-29.csv
./2023/01/Stocks-2023-01-30.csv
./2023/01/Stocks-2023-01-31.csv

Step 2: Read the CSV files into a DataFrame

# read the CSV files, {year}/{month}/{day}.csv

df_stocks = (
    spark.read.format("csv")
    .option("header", "true")
    .load(f"{DATA_FOLDER}/csv/*/*/*.csv")
)

df_stocks.tail(8)

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[Row(symbol='WHO', price='783.0', timestamp='2023-05-14 23:59:00'),
 Row(symbol='WHAT', price='592.25', timestamp='2023-05-14 23:59:00'),
 Row(symbol='IDK', price='639.0', timestamp='2023-05-14 23:59:00'),
 Row(symbol='WHY', price='626.36', timestamp='2023-05-14 23:59:00'),
 Row(symbol='BCUZ', price='969.7', timestamp='2023-05-14 23:59:00'),
 Row(symbol='TMRW', price='794.55', timestamp='2023-05-14 23:59:00'),
 Row(symbol='TDY', price='868.52', timestamp='2023-05-14 23:59:00'),
 Row(symbol='IDGD', price='809.99', timestamp='2023-05-14 23:59:00')]

# remove all but specified stock symbol
# individual models can be built for each stock

df_stocks = df_stocks.select("*").where(
    'symbol == "' + STOCK_SYMBOL + '"'
)

df_stocks.tail(4)

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[Row(symbol='WHO', price='791.68', timestamp='2023-05-14 23:56:00'),
 Row(symbol='WHO', price='780.36', timestamp='2023-05-14 23:57:00'),
 Row(symbol='WHO', price='785.9', timestamp='2023-05-14 23:58:00'),
 Row(symbol='WHO', price='783.0', timestamp='2023-05-14 23:59:00')]

# strictly speaking, we don't need to sort the dataframe, 
# but it can help for exploration of the data 

df_stocks = df_stocks.sort("timestamp")
df_stocks.tail(4)

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[Row(symbol='WHO', price='528.61', timestamp='2024-12-31 23:56:00'),
 Row(symbol='WHO', price='525.03', timestamp='2024-12-31 23:57:00'),
 Row(symbol='WHO', price='526.71', timestamp='2024-12-31 23:58:00'),
 Row(symbol='WHO', price='514.9', timestamp='2024-12-31 23:59:00')]

# include only historical data when building model

import datetime

currentdate = datetime.datetime.utcnow()
currentdate = currentdate.replace(hour=0, minute=0, second=0, microsecond=0)

# to manually specify a cutoff date in the data, specify the date below:
# currentdate = "2023-11-27 00:00:00"

df_stocks_history = df_stocks.select("*").where(
    'timestamp < "' + str(currentdate) + '"')

df_stocks_history.tail(4)

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[Row(symbol='WHO', price='876.89', timestamp='2023-11-27 23:56:00'),
 Row(symbol='WHO', price='872.61', timestamp='2023-11-27 23:57:00'),
 Row(symbol='WHO', price='874.65', timestamp='2023-11-27 23:58:00'),
 Row(symbol='WHO', price='879.56', timestamp='2023-11-27 23:59:00')]

# convert to a pandas dataframe, and rename the columns to 'ds' and 'y'  
# for time and label/outcome columns

import pandas as pd

dfstocks_pd = df_stocks_history.toPandas()

# rename the columns as expected by Prophet (ds and y)
dfstocks_pd = dfstocks_pd.rename(columns={'timestamp': 'ds'})
dfstocks_pd = dfstocks_pd.rename(columns={'price': 'y'})

# verify max/min timestamps in the dataframe, as the tail/head data may not be in order
print('Min: ', dfstocks_pd['ds'].min())
print('Max: ', dfstocks_pd['ds'].max())

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Min:  2023-01-01 00:00:00
Max:  2023-11-27 23:59:00

Step 3: Train the model

In developing a model, we'll use Prophet developed by Facebook's Core Data Science team. Prophet is ideal for forecasting time series data. Prophet excels at simplicity, so this is an ideal starting point as it limits any feature engineering and variables.

# Prophet variables

changepoint_prior_scale = 0.05
changepoint_range = 0.95
seasonality_prior_scale = 10
weekly_seasonality = 5

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from prophet import Prophet
from prophet.plot import add_changepoints_to_plot

m = Prophet(changepoint_prior_scale = changepoint_prior_scale, 
    changepoint_range = changepoint_range, 
    seasonality_prior_scale = seasonality_prior_scale,
    weekly_seasonality=weekly_seasonality)
m.fit(dfstocks_pd)
future = m.make_future_dataframe(periods=60*24*7, freq='min', include_history = False)
forecast = m.predict(future)
fig = m.plot(forecast)

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Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
input tempfile: /tmp/tmpx_vihtqe/qb3s21xt.json
input tempfile: /tmp/tmpx_vihtqe/trlc__us.json
idx 0
running CmdStan, num_threads: None
CmdStan args: ['/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=53724', 'data', 'file=/tmp/tmpx_vihtqe/qb3s21xt.json', 'init=/tmp/tmpx_vihtqe/trlc__us.json', 'output', 'file=/tmp/tmpx_vihtqe/prophet_modelq7j578rl/prophet_model-20231128193654.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']
Chain [1] start processing
Chain [1] done processing

19:36:54 - cmdstanpy - INFO - Chain [1] start processing
19:39:20 - cmdstanpy - INFO - Chain [1] done processing

fig = m.plot(forecast)
a = add_changepoints_to_plot(fig.gca(), m, forecast)

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fig2 = m.plot_components(forecast)

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# show the first few rows of the forecast
# yhat is predicted value of y at the given ds

forecast.head()

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	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	additive_terms	additive_terms_lower	additive_terms_upper	daily	daily_lower	daily_upper	weekly	weekly_lower	weekly_upper	multiplicative_terms	multiplicative_terms_lower	multiplicative_terms_upper	yhat
0	2023-11-28 00:00:00	850.832209	727.530092	1009.326073	850.832209	850.832209	23.561510	23.561510	23.561510	-89.809929	-89.809929	-89.809929	113.371439	113.371439	113.371439	0.0	0.0	0.0	874.393719
1	2023-11-28 00:01:00	850.831954	737.926558	1019.525300	850.831954	850.831954	23.066960	23.066960	23.066960	-90.273094	-90.273094	-90.273094	113.340054	113.340054	113.340054	0.0	0.0	0.0	873.898914
2	2023-11-28 00:02:00	850.831698	726.055640	1019.743695	850.831698	850.831698	22.582459	22.582459	22.582459	-90.725785	-90.725785	-90.725785	113.308244	113.308244	113.308244	0.0	0.0	0.0	873.414157
3	2023-11-28 00:03:00	850.831443	731.720294	1012.522697	850.831443	850.831443	22.108060	22.108060	22.108060	-91.167947	-91.167947	-91.167947	113.276007	113.276007	113.276007	0.0	0.0	0.0	872.939503
4	2023-11-28 00:04:00	850.831188	733.006903	1013.515838	850.831188	850.831188	21.643816	21.643816	21.643816	-91.599528	-91.599528	-91.599528	113.243343	113.243343	113.243343	0.0	0.0	0.0	872.475004

import plotly.express as px

fig = px.line(forecast, x='ds', y='yhat')
fig.update_layout(title="Trend of predicted (yhat) over time", showlegend=True)
fig.show()

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Step 4: Cross Validate

The purpose of cross validation is to simplify the process of separating training and test data. This allows us to test many points in time for accuracy, and also allows us to include all data in our model.

For cross validation, the initial period parameter is used to train the cross validation model, forecasting for the specified horizon. The next validation will occur over the next specified period.

So, if we wanted to validate the most recent 2 weeks, we'd specify the number of days in our training set minus 14 days, then specify a horizon and period of 7 days each. This will result in 2 validation forecasts: one for last week, and one for the week prior.

More information on cross validation is available in the Prophet docs.

# calculate the number of days to validate

from datetime import datetime

minDate = datetime.strptime(dfstocks_pd['ds'].min(), '%Y-%m-%d %H:%M:%S') 
maxDate = datetime.strptime(dfstocks_pd['ds'].max(), '%Y-%m-%d %H:%M:%S') 

numDays = (maxDate - minDate).days
numDaysToValidate = numDays - 14

print(numDaysToValidate)

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316

from prophet.diagnostics import cross_validation
from prophet.diagnostics import performance_metrics

df_cv = cross_validation(m, initial=f"{numDaysToValidate} days", period="7 days", horizon="7 days")

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Making 2 forecasts with cutoffs between 2023-11-13 23:59:00 and 2023-11-20 23:59:00

  0%|          | 0/2 [00:00<?, ?it/s]

input tempfile: /tmp/tmpx_vihtqe/rh28au_6.json
input tempfile: /tmp/tmpx_vihtqe/yiwxk0xf.json
idx 0
running CmdStan, num_threads: None
CmdStan args: ['/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=68077', 'data', 'file=/tmp/tmpx_vihtqe/rh28au_6.json', 'init=/tmp/tmpx_vihtqe/yiwxk0xf.json', 'output', 'file=/tmp/tmpx_vihtqe/prophet_modeljq8yhw_x/prophet_model-20231128193958.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']
Chain [1] start processing
Chain [1] done processing
input tempfile: /tmp/tmpx_vihtqe/i91ycjhf.json
input tempfile: /tmp/tmpx_vihtqe/ntd0vusb.json
idx 0
running CmdStan, num_threads: None
CmdStan args: ['/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=7497', 'data', 'file=/tmp/tmpx_vihtqe/i91ycjhf.json', 'init=/tmp/tmpx_vihtqe/ntd0vusb.json', 'output', 'file=/tmp/tmpx_vihtqe/prophet_modelfxp2tma8/prophet_model-20231128194311.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']
Chain [1] start processing
Chain [1] done processing

19:39:58 - cmdstanpy - INFO - Chain [1] start processing
19:42:53 - cmdstanpy - INFO - Chain [1] done processing
19:43:11 - cmdstanpy - INFO - Chain [1] start processing
19:45:56 - cmdstanpy - INFO - Chain [1] done processing

df_cv.tail(4)

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	ds	yhat	yhat_lower	yhat_upper	y	cutoff
20156	2023-11-27 23:56:00	900.506915	750.040613	1045.328524	876.89	2023-11-20 23:59:00
20157	2023-11-27 23:57:00	899.972146	758.399509	1051.470874	872.61	2023-11-20 23:59:00
20158	2023-11-27 23:58:00	899.447196	760.243681	1045.334076	874.65	2023-11-20 23:59:00
20159	2023-11-27 23:59:00	898.932128	750.350050	1050.692995	879.56	2023-11-20 23:59:00

# generate metrics using the default rolling window (10%)

from prophet.diagnostics import performance_metrics
df_p = performance_metrics(df_cv)
df_p.head()

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	horizon	mse	rmse	mae	mape	mdape	smape	coverage
0	0 days 16:48:00	6167.009558	78.530310	63.174213	0.068276	0.060806	0.069019	0.942956
1	0 days 16:49:00	6171.119103	78.556471	63.212545	0.068321	0.060864	0.069065	0.942956
2	0 days 16:50:00	6175.168024	78.582237	63.253430	0.068371	0.060953	0.069114	0.942956
3	0 days 16:51:00	6180.266741	78.614673	63.303327	0.068431	0.061057	0.069174	0.942956
4	0 days 16:52:00	6186.019853	78.651255	63.347925	0.068485	0.061143	0.069228	0.942956

# generate metrics using all data (100%)

from prophet.diagnostics import performance_metrics
df_p = performance_metrics(df_cv, rolling_window=1)
df_p.head()

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	horizon	mse	rmse	mae	mape	mdape	smape	coverage
0	7 days	12580.251284	112.161719	90.712569	0.114943	0.089969	0.10911	0.786905

# get the statistics for storing in the model
# this becomes part of the model's metadata

mse = df_p['mse'][0]
mae = df_p['mae'][0]
rmse = df_p['rmse'][0]
mape = df_p['mape'][0]

print('mse:', mse)
print('mae:', mae)
print('rmse:', rmse)
print('mape:', mape)

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mse: 12580.251284461425
mae: 90.71256901757745
rmse: 112.16171933623978
mape: 0.11494253209995128

# plot the mean absolute percent error (mape)

from prophet.plot import plot_cross_validation_metric
fig = plot_cross_validation_metric(df_cv, metric='mape')

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# plot the standard deviation or root mean square deviation (rmse)

from prophet.plot import plot_cross_validation_metric
fig = plot_cross_validation_metric(df_cv, metric='rmse')

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# routine for testing multiple combinations of parameters

# by default we won't run this, but this is a way we can test multiple
# parameters and find the most optimal results

# more info at:
# https://facebook.github.io/prophet/docs/diagnostics.html

import itertools
import numpy as np
import pandas as pd

param_grid = {  
    'changepoint_prior_scale': [0.001, 0.1, 0.5],
    'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0, 15],
    'weekly_seasonality': [3, 5]
}

test_parameters = False # change to True to run series of tests. 

if test_parameters:
    daysToValidate = numDays - 7

    # generate all combinations of parameters
    all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]
    rmses = []  # Ssore the RMSEs for each params here

    # use cross validation to evaluate all parameters
    for params in all_params:
        
        m = Prophet(**params).fit(dfstocks_pd)  # fit model with given params
        df_cv = cross_validation(m, initial=f"{daysToValidate} days", period="7 days", horizon="7 days")
        df_p = performance_metrics(df_cv, rolling_window=1)
        rmses.append(df_p['rmse'].values[0])

    # Find the best parameters
    tuning_results = pd.DataFrame(all_params)
    tuning_results['rmse'] = rmses
    print(tuning_results)

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Step 5: Log and load model with MLflow

MLflow assists with managing ML workflows. We can create a new experiment for each stock (as an example) and then add each run to the experiment. We can also log all of the parameters and metrics with each run, allowing us to see and compare different models. This is part of our operationalizing process. When the run is logged in the experiment, the run is given a URI that can be used to load the model later; however, it's also possible to interact with MLflow visually or programmatically to load/inspect models.

# setup mlflow with an experiment

import mlflow

EXPERIMENT_NAME = STOCK_SYMBOL + "-stock-prediction"
mlflow.set_experiment(EXPERIMENT_NAME)

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2023/11/28 19:46:16 INFO mlflow.tracking.fluent: Experiment with name 'WHO-stock-prediction' does not exist. Creating a new experiment.

<Experiment: artifact_location='', creation_time=1701200780477, experiment_id='bc2f76aa-65a8-4ec1-b163-25cbdddbdb8d', last_update_time=None, lifecycle_stage='active', name='WHO-stock-prediction', tags={}>

from mlflow.models.signature import infer_signature

model_name = f"{EXPERIMENT_NAME}-model"
with mlflow.start_run() as run:
    mlflow.autolog()

    mlflow.prophet.log_model(m, model_name, registered_model_name=model_name,
        signature=infer_signature(future, forecast))

    mlflow.log_params({"changepoint_prior_scale": changepoint_prior_scale })
    mlflow.log_params({"changepoint_range": changepoint_range })
    mlflow.log_params({"seasonality_prior_scale": seasonality_prior_scale })

    mlflow.log_metrics({"mse":mse})
    mlflow.log_metrics({"mae":mae})
    mlflow.log_metrics({"rmse":rmse})
    mlflow.log_metrics({"mape":mape})

    model_uri = f"runs:/{run.info.run_id}/{model_name}"

    print("Model saved in run %s" % run.info.run_id)
    print(f"Model URI: {model_uri}")

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/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning:

Setuptools is replacing distutils.

Successfully registered model 'WHO-stock-prediction-model'.
2023/11/28 19:46:32 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation. Model name: WHO-stock-prediction-model, version 1
Created version '1' of model 'WHO-stock-prediction-model'.

Model saved in run bb0419ef-61f4-44ed-a98e-9112dbd55916
Model URI: runs:/bb0419ef-61f4-44ed-a98e-9112dbd55916/WHO-stock-prediction-model

Step 6: Load the model and generate predictions

In this step, we'll load the model from MLflow and create a new prediction for the next week.

Because this is a simulation for demo purposes, we already have the future data (or at least, one possibility of future data as the algorithm is highly random). This allows us to compare what the model predicts vs actual. We'll combine the predicted dataset with the actual dataset, and plot the outcome to compare.

import mlflow

loaded_model = mlflow.prophet.load_model(model_uri)

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Downloading artifacts:   0%|          | 0/5 [00:00<?, ?it/s]

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# establish begin/end dates for prediction

import datetime
from datetime import timedelta

currentdate = datetime.datetime.utcnow()
currentdate = currentdate.replace(hour=0, minute=0, second=0, microsecond=0)
enddate = currentdate + datetime.timedelta(days=7)

print(f'Beginning of forecast: {currentdate}')
print(f'End of forecast: {enddate}')

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Beginning of forecast: 2023-11-28 00:00:00
End of forecast: 2023-12-05 00:00:00

# load all of the 'future' data -- 
# will be used to compare prediction

df_stocks_future = df_stocks.select("*").where(
    'timestamp >= "' + str(currentdate) + '" and ' +
    'timestamp < "' + str(enddate) + '"')

df_stocks_future.tail(4)

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[Row(symbol='WHO', price='846.94', timestamp='2023-12-04 23:56:00'),
 Row(symbol='WHO', price='862.69', timestamp='2023-12-04 23:57:00'),
 Row(symbol='WHO', price='877.17', timestamp='2023-12-04 23:58:00'),
 Row(symbol='WHO', price='870.12', timestamp='2023-12-04 23:59:00')]

import pandas as pd

# create a new dataframe to hold the predictions
# copy the timestamp from the future dataframe for convenience
# this new dataframe should only have timestamps for our prediction, 
# and will be labelled as 'ds'

df_stocks_future_pd = df_stocks_future.toPandas()
dfstocks_predict = df_stocks_future_pd[['timestamp']].copy()

# rename timestamp to ds as expected by prophet
dfstocks_predict = dfstocks_predict.rename(columns={'timestamp': 'ds'})

print('Min: ', dfstocks_predict['ds'].min())
print('Max: ', dfstocks_predict['ds'].max())
dfstocks_predict.head()

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Min:  2023-11-28 00:00:00
Max:  2023-12-04 23:59:00

	ds
0	2023-11-28 00:00:00
1	2023-11-28 00:01:00
2	2023-11-28 00:02:00
3	2023-11-28 00:03:00
4	2023-11-28 00:04:00

# optionally, can use make_future_dataframe in Prophet to make a suitable df

test_df = loaded_model.make_future_dataframe(periods=60*24*7, freq='min', include_history = False)
test_df.head()

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	ds
0	2023-11-28 00:00:00
1	2023-11-28 00:01:00
2	2023-11-28 00:02:00
3	2023-11-28 00:03:00
4	2023-11-28 00:04:00

# predict by passing in the dataframe with timestamps to forecast

forecast = loaded_model.predict(dfstocks_predict)
forecast.head()

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	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	additive_terms	additive_terms_lower	additive_terms_upper	daily	daily_lower	daily_upper	weekly	weekly_lower	weekly_upper	multiplicative_terms	multiplicative_terms_lower	multiplicative_terms_upper	yhat
0	2023-11-28 00:00:00	850.832209	739.287059	1022.666715	850.832209	850.832209	23.561510	23.561510	23.561510	-89.809929	-89.809929	-89.809929	113.371439	113.371439	113.371439	0.0	0.0	0.0	874.393719
1	2023-11-28 00:01:00	850.831954	730.814867	1013.416578	850.831954	850.831954	23.066960	23.066960	23.066960	-90.273094	-90.273094	-90.273094	113.340054	113.340054	113.340054	0.0	0.0	0.0	873.898914
2	2023-11-28 00:02:00	850.831698	731.017916	1023.975112	850.831698	850.831698	22.582459	22.582459	22.582459	-90.725785	-90.725785	-90.725785	113.308244	113.308244	113.308244	0.0	0.0	0.0	873.414157
3	2023-11-28 00:03:00	850.831443	725.796840	1017.071866	850.831443	850.831443	22.108060	22.108060	22.108060	-91.167947	-91.167947	-91.167947	113.276007	113.276007	113.276007	0.0	0.0	0.0	872.939503
4	2023-11-28 00:04:00	850.831188	729.194123	1025.868951	850.831188	850.831188	21.643816	21.643816	21.643816	-91.599528	-91.599528	-91.599528	113.243343	113.243343	113.243343	0.0	0.0	0.0	872.475004

# combine forecast and df_stocks_future_pd

df_stocks_future_pd['timestamp'] = pd.to_datetime(df_stocks_future_pd['timestamp'])
forecast['ds'] = pd.to_datetime(forecast['ds'])

metric_df = forecast.set_index('ds')[['yhat']].join(df_stocks_future_pd.set_index('timestamp').price).reset_index()
metric_df.head()

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	ds	yhat	price
0	2023-11-28 00:00:00	874.393719	881.0
1	2023-11-28 00:01:00	873.898914	865.43
2	2023-11-28 00:02:00	873.414157	852.22
3	2023-11-28 00:03:00	872.939503	848.68
4	2023-11-28 00:04:00	872.475004	864.64

import plotly.express as px
import plotly.graph_objects as go

metric_df['ds'] = pd.to_datetime(metric_df['ds'])

fig = go.Figure()
fig.add_trace(go.Scatter(x=metric_df['ds'], y=metric_df['price'], name='Actual', line=dict(color='blue', width=1)))
fig.add_trace(go.Scatter(x=metric_df['ds'], y=metric_df['yhat'], name='Predicted', line=dict(color='red', width=3)))

fig.update_layout(title="Predicted vs Actual", showlegend=True)
fig.show()

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# optionally, compute metrics using sklearn to see how they compare to our model

from sklearn.metrics import mean_squared_error, mean_absolute_error

future_mse = round(mean_squared_error(metric_df.price, metric_df.yhat),3)
future_mae = round(mean_absolute_error(metric_df.price, metric_df.yhat),3)
future_rmse = round(mean_squared_error(metric_df.price, metric_df.yhat, squared=False),3)

print(f'mse: {future_mse} (Model: {mse})')
print(f'mae: {future_mae} (Model: {mae})')
print(f'rmse: {future_rmse} (Model: {rmse})')

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2023/11/28 19:47:23 INFO mlflow.tracking.fluent: Autologging successfully enabled for sklearn.

mse: 13326.014 (Model: 12580.251284461425)
mae: 94.35 (Model: 90.71256901757745)
rmse: 115.438 (Model: 112.16171933623978)

Additional MLflow

MLflow can be interacted with programmatically to load and inspect models. The example below will show all experiments and runs within each experiment.

import mlflow
import pandas as pd

experiments = mlflow.search_experiments()
for exp in experiments:
    print(f'{exp.name} ({exp.experiment_id})')
    runs_df = mlflow.search_runs(exp.experiment_id)
    display(runs_df)

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WHO-stock-prediction (bc2f76aa-65a8-4ec1-b163-25cbdddbdb8d)

SynapseWidget(Synapse.DataFrame, 2620bfb5-5f8e-45f4-9256-a4c674851f19)